The team proposes “Learned Reverse ISP with soft supervision" [1]}, an efficient network and a new loss function for the reversed ISP learning. They propose an encoder-decoder architecture called SSDNet inspired by the Transformer [2]}, [3]} architecture and is straightforward. The method is illustrated in Figure REF . They exploit the unique property of RAW data, i.e. high dynamic range (HDR), by suggesting to relax the supervision to a multivariate Gaussian distribution in order to learn images that are reasonable for a given supervision. Equipped with the above two components, the method achieves an effective RGB to RAW mapping.
We must note that the team uses the DeepISP S7 dataset [4]}, besides the proposed challenge dataset, this implies that the results are not accurate for this method, since they have potentially trained with extra validation data, nevertheless this should not affect the results in the “Test2" sets or “Track2".
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